<h1 id="florida-congressional-districts">2010 Florida Congressional
Districts</h1>
<h2 id="redistricting-requirements">Redistricting requirements</h2>
<p>In Florida, per Art. III, sec. 20 of the <a
href="http://www.leg.state.fl.us/Statutes/index.cfm?Mode=Constitution&amp;Submenu=3#A3S16">state
constitution</a>, districts must:</p>
<ol type="1">
<li>may not favor or disfavor political parties or incumbents</li>
<li>may not be drawn with the intent or result of denying or diluting
minority representation</li>
<li>must be contiguous</li>
<li>must be compact</li>
<li>must be equal in population as practicable</li>
<li>and, must utilize, where feasible, existing political and
geographical boundaries.</li>
</ol>
<h3 id="algorithmic-constraints">Algorithmic Constraints</h3>
<p>We enforce a maximum population deviation of 0.5%.</p>
<h2 id="data-sources">Data Sources</h2>
<p>Data for Florida comes from the ALARM Project’s <a
href="https://alarm-redist.github.io/posts/2021-08-10-census-2020/">2010
Redistricting Data Files</a>. We obtain the 2010 Florida Congressional
map from <a
href="https://redistricting.lls.edu/state/florida/?cycle=2010&amp;level=Congress&amp;startdate=2012-04-30">All
About Redistricting</a>.</p>
<h2 id="pre-processing-notes">Pre-processing Notes</h2>
<p>We estimate CVAP populations with the <a
href="https://github.com/christopherkenny/cvap">cvap</a> R package.</p>
<h2 id="simulation-notes">Simulation Notes</h2>
<p>We sample 35,000 districting plans for the full state of Florida,
thinned down to a set of 7,500. To appropriately district the entire
state, we split the state into three regions, simulate two of the
regions (North and the Miami area, as defined below) separately, and
then simulate districts in the remainder of the state. In all
simulations, we constrain county and municipality splits. Since some
county populations are greater than the target population for one
Congressional district, we create pseudocounties where needed.</p>
<p><strong>Regional clustering:</strong> We split Florida into the
following three regions:</p>
<ol type="1">
<li>Miami metropolitan area, consisting of Miami-Dade and Broward
Counties.</li>
<li>Northern Florida, consisting of Alachua County, Baker County, Bay
County, Bradford County, Calhoun County, Citrus County, Clay County,
Columbia County, Dixie County, Duval County, Escambia County, Flagler
County, Franklin County, Gadsden County, Gilchrist County, Gulf County,
Hamilton County, Holmes County, Jackson County, Jefferson County,
Lafayette County, Leon County, Levy County, Liberty County, Madison
County, Marion County, Nassau County, Okaloosa County, Putnam County,
St. Johns County, Santa Rosa County, Sumter County, Suwannee County,
Taylor County, Union County, Volusia County, Wakulla County, Walton
County, and Washington County.</li>
<li>Central Florida, composed of Brevard County, Charlotte County,
Collier County, DeSoto County, Glades County, Hardee County, Hendry
County, Hernando County, Highlands County, Hillsborough County, Indian
River County, Lake County, Lee County, Manatee County, Martin County,
Monroe County, Okeechobee County, Orange County, Osceola County, Palm
Beach County, Pasco County, Pinellas County, Polk County, St. Lucie
County, Sarasota County, and Seminole County.</li>
</ol>
<p>We simulate the Miami metropolitan area and Northern Florida
independently. Since each cluster has leftover population, we include a
constraint to encourage unassigned precincts to be set along each
cluster’s boundary with Central Florida so those precincts can be
assigned to contiguous districts in the final simulation step.</p>
<p><strong>Simulating Miami:</strong> We simulate four SMC runs with
60,000 maps each for the Miami metropolitan area. To encourage Black and
Hispanic opportunity districts, we apply Gibbs constraints in the
simulation. We then subset down the plans to those where there exists
one district with a Black voting-age population (BVAP) share of at least
.4 and another district with a BVAP share of at least .25. From this
set, we randomly sample 35,000 plans.</p>
<p><strong>Simulating Northern Florida:</strong> We simulate two SMC
runs with 40,000 maps each for Northern Florida. To encourage Black and
Hispanic opportunity districts, we apply Gibbs constraints in the
simulation. We then subset down the plans to those where at least one
district has a BVAP share of .25 or greater. From this set, we randomly
sample 35,000 plans.</p>
<p><strong>Simulating Central Florida:</strong> Using the unassigned
areas from the partial SMC simulations for Miami and Northern Florida,
we simulate two SMC runs with 35,000 plans each for Central Florida. We
apply Gibbs constraints to encourage Black and Hispanic opportunity
districts. We then thin these maps down to the final set of 5,000.</p>
<h2 id="contents">Contents</h2>
<ul>
<li><code>FL_cd_2010_stats.csv</code> contains summary statistics on the
sampled redistricting plans</li>
<li><code>FL_cd_2010_plans.rds</code> is a compressed
<code>redist_plans</code> object, which contains the matrix of
precinct/block assignments and may be used for further analysis.</li>
<li><code>FL_cd_2010_map.rds</code> is a compressed
<code>redist_map</code> object, which contains the precinct/block
shapefile and demographic data.</li>
</ul>
<p>Both the <code>redist_plans</code> and <code>redist_map</code> object
are intended to be used with the <a
href="https://alarm-redist.github.io/redist/">redist package</a>.</p>
<h3 id="codebook-for-summary-statistics">Codebook for summary
statistics</h3>
<ul>
<li><code>draw</code>: unique identifier for each sample. Non-numeric
draw names are real-world plans, e.g., <code>cd_2010</code> for an
enacted 2010 plan.</li>
<li><code>district</code>: a district identifier. District numbers
roughly match those in the enacted plan, but the correspondence is not
perfect.</li>
<li><code>chain</code>: a number identifying the run of the
redistricting algorithm used to produce this draw. Used for diagnostic
purposes.</li>
<li><code>pop_overlap</code>: a number indicating the fraction of people
in this plan who reside in the same-numbered district in the enacted
plan.</li>
<li><code>total_pop</code>: the total population of each district.</li>
<li><code>total_vap</code>: the total voting-aged population of each
district.</li>
<li><code>pop_*</code>, <code>vap_*</code>: total (voting-aged)
population within racial and ethnic groups for each district. Variable
codes documented <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>plan_dev</code>: the maximum population deviation among
districts in the plan. Computed as
<code>max(abs(distr_pop - target_pop)/target_pop)</code>.</li>
<li><code>comp_edge</code>: compactness, as measured by the fraction of
internal edges kept. Higher values indicate more compactness.</li>
<li><code>comp_polsby</code>: compactness, as measured by the
Polsby-Popper score. Higher values indicate more compactness.</li>
<li><code>county_splits</code>: the number of counties which belong to
more than one district.</li>
<li><code>muni_splits</code>: the number of Census Designated Places
which belong to more than one district.</li>
<li><code>*_##_dem_*</code>, <code>*_##_rep_*</code>: vote counts for
statewide Democratic and Republican candidates in a certain election.
More information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>adv_##</code>, <code>arv_##</code>: average vote counts for
statewide Democratic and Republican candidates in a certain year. More
information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>ndv</code>, <code>nrv</code>: averages of the
<code>adv_##</code> and <code>arv_##</code> variables across all
available elections.</li>
<li><code>ndshare</code>: normal Democratic share, computed as
<code>ndv / (ndv + nrv)</code></li>
<li><code>e_dvs</code>: average Democratic vote share, computed as the
average of the Democratic vote share when first scored under each
statewide election.</li>
<li><code>pr_dem</code>: probability seat is represented by a Democrat;
calculated as the fraction of statewide elections under which the
district had a majority Democratic share.</li>
<li><code>e_dem</code>: expected number of Democratic seats for the
plan; equivalent to summing the <code>pr_dem</code> values across
districts</li>
<li><code>pbias</code>: partisan bias at 50% vote share, averaged across
all available elections. Positive values indicate Republican bias.</li>
<li><code>egap</code>: the efficiency gap, averaged across all available
elections. Positive values indicate Republican bias.</li>
</ul>
